Time Series Forecast for Rainfall Intensity in Malang City with Naive Bayes Methodology

Muthmainnah, Mahmuda; Ashar, Muhammad; Wirawan, I Made; Widiyaningtyas, Triyanna

doi:10.1109/siet.2018.8693171

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…The evaluation that will be used is the Confusion Matrix. By knowing the total prediction is correct, it is possible to see the accuracy value [14]. The confusion matrix can be seen in Table I.…”

Section: Discussionmentioning

confidence: 99%

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Prediction and Mapping Rainfall Classification Using Naive Bayes and Simple Kriging

Yoga

Prasetyowati

Sibaroni

2022

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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This study discusses the development of a prediction model for the classification of rainfall based on time in Java. The method used in this research is naive Bayes and simple kriging. Naive Bayes is used for classification prediction, while simple kriging is an interpolation method used for mapping. There are two scenarios used, that is building a prediction model for daily and monthly rainfall classification, with data taken from 27 weather stations on the island of Java from 2010 to 2021. The results obtained in the classification process are an accuracy value of 67% for the daily model and 88% for the monthly model. The daily model data uses a spherical semivariogram with an average RMSE of 1,021. For the monthly model data using a Gaussian semivariogram with an average RMSE of 0,34. Then interpolation using simple kriging for mapping rainfall. The results of this study are predictions for the classification and mapping of daily rainfall models from April 1 to April 7 2022 and monthly models from April to September 2022. The contribution of this research is to provide predictive information and mapping of future rainfall so that public people can anticipate more.

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Section: Discussionmentioning

confidence: 99%

“…The nave Bayes method yields the validity of probability theory, but cannot perform the inferentiality of many closely related rules. The advantage of the nave Bayes method is that it produces a fairly high accuracy value [14]. The nave Bayes equation can be seen in (1).…”

Section: Naïve Bayesmentioning

confidence: 99%

Prediction and Mapping Rainfall Classification Using Naive Bayes and Simple Kriging

Yoga

Prasetyowati

Sibaroni

2022

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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“…The Naïve Bayes method is used to calculate the probability of an event occurring in the future based on previous experience [51], [52]. Naïve Bayes analysis was used to classify the test results of the 20 soil samples to obtain the paddy soil phosphorus status.…”

Section: Naïve Bayes Analysismentioning

confidence: 99%

Spatial distribution of soil nutrient content for sustainable rice agriculture using geographic information system and Naïve Bayes classifier

Yudhana

Cahyo

Sabila

et al. 2023

International Journal on Smart Sensing and Intelligent Systems

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This study aims to assist farmers in monitoring soil nutrients, especially phosphorus. To measure the phosphorus content of paddy soil, the TCS3200 converter, as an intelligent sensor, was applied. The geographical information system (GIS) was also involved in this research to map the phosphorus content. In addition, the Naïve Bayes method was applied to classify lowland soil phosphorus status. The result of this study indicated that the Naïve Bayes algorithm could classify lowland soil phosphorus status with a probability of 0.34 for moderate phosphorus conditions and 0.66 for high phosphorus conditions. The sample testing results showed that the error rate was 3% and the success rate was 97%. Testing with a phosphorus-measuring instrument can be carried out by mapping the soil phosphorus status with the ArcGIS software, whereby seven points of medium-phosphorus-status paddy soil and 13 locations of high-phosphorus-status soil samples were determined. This research thus successfully mapped the soil phosphorus.

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“…Penelitian yang terkait dengan penggunaan data mining untuk prediksi/klasifikasi curah hujan telah banyak dilakukan oleh para peneliti dengan berbagai algoritma dengan parameter data yang berbeda diantaranya oleh : [4] mengenai curah hujan di kota Malang menggunakan Naïve Bayes mendapat hasil 97.74% akurasi, [5] melakukan klasifikasi curah hujan menggunakan algortima C4.5 di Kabupaten Bandung hasilnya adalah rata-rata nilai akurasi yang didapat 60% tanpa pruning dan 93.33% menggunakan pruning. [6] meneliti tentang estimasi curah hujan bulanan di Australia menggunakan metode ensemble hybrid Genetic Algorithm menghasilkan bahwa teknik ensemble menunjukan hasil akurasi terbaik.…”

Section: Pendahuluanunclassified

“…Naïve Bayes ditemukan oleh seorang ilmuwan Inggris yang bernama Thomas Bayes dan merupakan metode untuk memprediksi probabilitas kejadian di masa depan berdasarkan pengalaman sebelumnya. Salah satu keunggulan Naive Bayes adalah metode ini tidak membutuhkan data pelatihan yang banyak, dan hasil akurasinya cukup tinggi [4].…”

Section: Naïve Bayesunclassified

Penggunaan Stacking Classifier Untuk Prediksi Curah Hujan

Sidik

Sen

2019

itfs

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Curah hujan sebagai bentuk informasi dari data meteorologis, penting dalam segala kegiatan manusia yang berhubungan dengan alam, oleh karena itu prediksi atas curah hujan dengan hasil yang akurat merupakan hal yang sangat penting. Salah satu metode yang digunakan untuk prediksi/klasifikasi curah hujan adalah data mining dengan berbagai algoritma dan parameter data yang berbeda. Pada penelitian ini digunakan penggabungan metode klasifikasi dengan Teknik Ensemble Stacking/Stacked Generalization yang menggunakan Naïve Bayes dan C4.5 sebagai base learner dan KNN sebagai meta learner untuk klasifikasi curah hujan. Dataset yang dipergunakan adalah data klimatologi harian yang diambil dari website resmi BMKG (Badan Meteorologi, Klimatologi, Dan Geofisika) untuk stasiun UPT Bandung, Bogor, Citeko dan Jatiwangi dari periode 01 Januari 2000 sampai dengan 31 Desember 2018. Dengan menggunakan tiga skenario pengujian dan validasi menggunakan 10 fold cross validation diperoleh bahwa metode stacking dapat meningkatkan akurasi dari base classifier.

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Time Series Forecast for Rainfall Intensity in Malang City with Naive Bayes Methodology

Cited by 5 publications

References 4 publications

Prediction and Mapping Rainfall Classification Using Naive Bayes and Simple Kriging

Prediction and Mapping Rainfall Classification Using Naive Bayes and Simple Kriging

Spatial distribution of soil nutrient content for sustainable rice agriculture using geographic information system and Naïve Bayes classifier

Penggunaan Stacking Classifier Untuk Prediksi Curah Hujan

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